Overview

Dataset statistics

Number of variables17
Number of observations8693
Missing cells0
Missing cells (%)0.0%
Duplicate rows498
Duplicate rows (%)5.7%
Total size in memory976.6 KiB
Average record size in memory115.0 B

Variable types

Boolean2
Categorical6
Numeric9

Alerts

Dataset has 498 (5.7%) duplicate rowsDuplicates
VIP is highly imbalanced (84.3%)Imbalance
Age has 178 (2.0%) zerosZeros
RoomService has 5661 (65.1%) zerosZeros
FoodCourt has 5542 (63.8%) zerosZeros
ShoppingMall has 5702 (65.6%) zerosZeros
Spa has 5398 (62.1%) zerosZeros
VRDeck has 5585 (64.2%) zerosZeros
Consumption has 3805 (43.8%) zerosZeros

Reproduction

Analysis started2024-04-24 13:37:40.258580
Analysis finished2024-04-24 13:38:20.152543
Duration39.89 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

CryoSleep
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
False
5571 
True
3122 
ValueCountFrequency (%)
False 5571
64.1%
True 3122
35.9%
2024-04-24T15:38:20.752956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Destination
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
TRAPPIST-1e
6086 
55 Cancri e
1811 
PSO J318.5-22
796 

Length

Max length13
Median length11
Mean length11.183136
Min length11

Characters and Unicode

Total characters97215
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAPPIST-1e
2nd rowTRAPPIST-1e
3rd rowTRAPPIST-1e
4th rowTRAPPIST-1e
5th rowTRAPPIST-1e

Common Values

ValueCountFrequency (%)
TRAPPIST-1e 6086
70.0%
55 Cancri e 1811
 
20.8%
PSO J318.5-22 796
 
9.2%

Length

2024-04-24T15:38:20.979175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T15:38:21.217877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
trappist-1e 6086
46.4%
55 1811
 
13.8%
cancri 1811
 
13.8%
e 1811
 
13.8%
pso 796
 
6.1%
j318.5-22 796
 
6.1%

Most occurring characters

ValueCountFrequency (%)
P 12968
13.3%
T 12172
12.5%
e 7897
 
8.1%
S 6882
 
7.1%
- 6882
 
7.1%
1 6882
 
7.1%
A 6086
 
6.3%
I 6086
 
6.3%
R 6086
 
6.3%
5 4418
 
4.5%
Other values (13) 20856
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 12968
13.3%
T 12172
12.5%
e 7897
 
8.1%
S 6882
 
7.1%
- 6882
 
7.1%
1 6882
 
7.1%
A 6086
 
6.3%
I 6086
 
6.3%
R 6086
 
6.3%
5 4418
 
4.5%
Other values (13) 20856
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 12968
13.3%
T 12172
12.5%
e 7897
 
8.1%
S 6882
 
7.1%
- 6882
 
7.1%
1 6882
 
7.1%
A 6086
 
6.3%
I 6086
 
6.3%
R 6086
 
6.3%
5 4418
 
4.5%
Other values (13) 20856
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 12968
13.3%
T 12172
12.5%
e 7897
 
8.1%
S 6882
 
7.1%
- 6882
 
7.1%
1 6882
 
7.1%
A 6086
 
6.3%
I 6086
 
6.3%
R 6086
 
6.3%
5 4418
 
4.5%
Other values (13) 20856
21.5%

Age
Real number (ℝ)

ZEROS 

Distinct204
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.83621
Minimum0
Maximum79
Zeros178
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-04-24T15:38:21.453626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q120
median27
Q337
95-th percentile55
Maximum79
Range79
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.361932
Coefficient of variation (CV)0.49805199
Kurtosis0.14809039
Mean28.83621
Median Absolute Deviation (MAD)9
Skewness0.41898548
Sum250673.17
Variance206.26508
MonotonicityNot monotonic
2024-04-24T15:38:21.748095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 324
 
3.7%
18 320
 
3.7%
21 311
 
3.6%
19 293
 
3.4%
23 292
 
3.4%
22 291
 
3.3%
20 277
 
3.2%
26 268
 
3.1%
28 267
 
3.1%
27 259
 
3.0%
Other values (194) 5791
66.6%
ValueCountFrequency (%)
0 178
2.0%
1 67
 
0.8%
2 75
0.9%
3 75
0.9%
4 71
 
0.8%
5 33
 
0.4%
6 40
 
0.5%
7 52
 
0.6%
8 46
 
0.5%
8.451165929 1
 
< 0.1%
ValueCountFrequency (%)
79 3
 
< 0.1%
78 3
 
< 0.1%
77 2
 
< 0.1%
76 2
 
< 0.1%
75 4
< 0.1%
74 5
0.1%
73 7
0.1%
72 4
< 0.1%
71 7
0.1%
70 9
0.1%

VIP
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
False
8495 
True
 
198
ValueCountFrequency (%)
False 8495
97.7%
True 198
 
2.3%
2024-04-24T15:38:22.185192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

RoomService
Real number (ℝ)

ZEROS 

Distinct1370
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.60689
Minimum0
Maximum14327
Zeros5661
Zeros (%)65.1%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-04-24T15:38:22.589784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q356
95-th percentile1267.4
Maximum14327
Range14327
Interquartile range (IQR)56

Descriptive statistics

Standard deviation661.80771
Coefficient of variation (CV)2.9465156
Kurtosis65.875787
Mean224.60689
Median Absolute Deviation (MAD)0
Skewness6.3480617
Sum1952507.7
Variance437989.45
MonotonicityNot monotonic
2024-04-24T15:38:22.986125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5661
65.1%
1 117
 
1.3%
2 79
 
0.9%
3 61
 
0.7%
4 47
 
0.5%
5 28
 
0.3%
9 25
 
0.3%
8 24
 
0.3%
6 24
 
0.3%
14 21
 
0.2%
Other values (1360) 2606
30.0%
ValueCountFrequency (%)
0 5661
65.1%
1 117
 
1.3%
1.077766015 1
 
< 0.1%
2 79
 
0.9%
3 61
 
0.7%
4 47
 
0.5%
5 28
 
0.3%
6 24
 
0.3%
7 17
 
0.2%
8 24
 
0.3%
ValueCountFrequency (%)
14327 1
< 0.1%
9920 1
< 0.1%
8586 1
< 0.1%
8243 1
< 0.1%
8209 1
< 0.1%
8168 1
< 0.1%
8151 1
< 0.1%
8142 1
< 0.1%
8030 1
< 0.1%
7406 1
< 0.1%

FoodCourt
Real number (ℝ)

ZEROS 

Distinct1604
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean456.26185
Minimum0
Maximum29813
Zeros5542
Zeros (%)63.8%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-04-24T15:38:23.344310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q386
95-th percentile2749.4679
Maximum29813
Range29813
Interquartile range (IQR)86

Descriptive statistics

Standard deviation1600.43
Coefficient of variation (CV)3.5077006
Kurtosis73.868236
Mean456.26185
Median Absolute Deviation (MAD)0
Skewness7.1174832
Sum3966284.2
Variance2561376.1
MonotonicityNot monotonic
2024-04-24T15:38:23.718529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5542
63.8%
1 116
 
1.3%
2 75
 
0.9%
4 53
 
0.6%
3 53
 
0.6%
5 33
 
0.4%
6 31
 
0.4%
9 28
 
0.3%
7 27
 
0.3%
10 27
 
0.3%
Other values (1594) 2708
31.2%
ValueCountFrequency (%)
0 5542
63.8%
1 116
 
1.3%
2 75
 
0.9%
3 53
 
0.6%
4 53
 
0.6%
5 33
 
0.4%
6 31
 
0.4%
7 27
 
0.3%
8 20
 
0.2%
9 28
 
0.3%
ValueCountFrequency (%)
29813 1
< 0.1%
27723 1
< 0.1%
27071 1
< 0.1%
26830 1
< 0.1%
21066 1
< 0.1%
18481 1
< 0.1%
17958 1
< 0.1%
17901 1
< 0.1%
17687 1
< 0.1%
17432 1
< 0.1%

ShoppingMall
Real number (ℝ)

ZEROS 

Distinct1208
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.1985
Minimum0
Maximum23492
Zeros5702
Zeros (%)65.6%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-04-24T15:38:24.025544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q331
95-th percentile926
Maximum23492
Range23492
Interquartile range (IQR)31

Descriptive statistics

Standard deviation599.17016
Coefficient of variation (CV)3.459442
Kurtosis333.11011
Mean173.1985
Median Absolute Deviation (MAD)0
Skewness12.68106
Sum1505614.6
Variance359004.89
MonotonicityNot monotonic
2024-04-24T15:38:24.413090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5702
65.6%
1 153
 
1.8%
2 80
 
0.9%
3 59
 
0.7%
4 45
 
0.5%
5 38
 
0.4%
7 36
 
0.4%
6 34
 
0.4%
13 29
 
0.3%
9 28
 
0.3%
Other values (1198) 2489
28.6%
ValueCountFrequency (%)
0 5702
65.6%
1 153
 
1.8%
2 80
 
0.9%
3 59
 
0.7%
4 45
 
0.5%
5 38
 
0.4%
6 34
 
0.4%
7 36
 
0.4%
8 28
 
0.3%
9 28
 
0.3%
ValueCountFrequency (%)
23492 1
< 0.1%
12253 1
< 0.1%
10705 1
< 0.1%
10424 1
< 0.1%
9058 1
< 0.1%
7810 1
< 0.1%
7185 1
< 0.1%
7148 1
< 0.1%
7104 1
< 0.1%
6805 1
< 0.1%

Spa
Real number (ℝ)

ZEROS 

Distinct1436
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.26865
Minimum0
Maximum22408
Zeros5398
Zeros (%)62.1%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-04-24T15:38:24.780347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q371
95-th percentile1611.4
Maximum22408
Range22408
Interquartile range (IQR)71

Descriptive statistics

Standard deviation1127.6609
Coefficient of variation (CV)3.6227897
Kurtosis82.114663
Mean311.26865
Median Absolute Deviation (MAD)0
Skewness7.6631181
Sum2705858.4
Variance1271619.1
MonotonicityNot monotonic
2024-04-24T15:38:25.105792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5398
62.1%
1 146
 
1.7%
2 105
 
1.2%
3 53
 
0.6%
5 53
 
0.6%
4 46
 
0.5%
7 34
 
0.4%
6 33
 
0.4%
9 28
 
0.3%
8 28
 
0.3%
Other values (1426) 2769
31.9%
ValueCountFrequency (%)
0 5398
62.1%
1 146
 
1.7%
2 105
 
1.2%
3 53
 
0.6%
4 46
 
0.5%
4.560684576 1
 
< 0.1%
5 53
 
0.6%
5.642490463 1
 
< 0.1%
6 33
 
0.4%
7 34
 
0.4%
ValueCountFrequency (%)
22408 1
< 0.1%
18572 1
< 0.1%
16594 1
< 0.1%
16139 1
< 0.1%
15586 1
< 0.1%
15331 1
< 0.1%
15238 1
< 0.1%
14970 1
< 0.1%
13995 1
< 0.1%
13902 1
< 0.1%

VRDeck
Real number (ℝ)

ZEROS 

Distinct1404
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.61396
Minimum0
Maximum24133
Zeros5585
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-04-24T15:38:25.398781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q352.407262
95-th percentile1514
Maximum24133
Range24133
Interquartile range (IQR)52.407262

Descriptive statistics

Standard deviation1136.9149
Coefficient of variation (CV)3.7446067
Kurtosis86.890833
Mean303.61396
Median Absolute Deviation (MAD)0
Skewness7.8473487
Sum2639316.2
Variance1292575.5
MonotonicityNot monotonic
2024-04-24T15:38:25.755201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5585
64.2%
1 139
 
1.6%
2 70
 
0.8%
3 56
 
0.6%
5 51
 
0.6%
4 47
 
0.5%
6 32
 
0.4%
8 30
 
0.3%
7 29
 
0.3%
9 25
 
0.3%
Other values (1394) 2629
30.2%
ValueCountFrequency (%)
0 5585
64.2%
1 139
 
1.6%
2 70
 
0.8%
3 56
 
0.6%
4 47
 
0.5%
5 51
 
0.6%
6 32
 
0.4%
7 29
 
0.3%
8 30
 
0.3%
9 25
 
0.3%
ValueCountFrequency (%)
24133 1
< 0.1%
20336 1
< 0.1%
17306 1
< 0.1%
17074 1
< 0.1%
16337 1
< 0.1%
14485 1
< 0.1%
12708 1
< 0.1%
12685 1
< 0.1%
12682 1
< 0.1%
12424 1
< 0.1%

Cabin_deck
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
F
2868 
G
2615 
E
877 
B
819 
C
768 
Other values (3)
746 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8693
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowF
3rd rowA
4th rowA
5th rowF

Common Values

ValueCountFrequency (%)
F 2868
33.0%
G 2615
30.1%
E 877
 
10.1%
B 819
 
9.4%
C 768
 
8.8%
D 483
 
5.6%
A 258
 
3.0%
T 5
 
0.1%

Length

2024-04-24T15:38:26.114310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T15:38:26.369063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
f 2868
33.0%
g 2615
30.1%
e 877
 
10.1%
b 819
 
9.4%
c 768
 
8.8%
d 483
 
5.6%
a 258
 
3.0%
t 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
F 2868
33.0%
G 2615
30.1%
E 877
 
10.1%
B 819
 
9.4%
C 768
 
8.8%
D 483
 
5.6%
A 258
 
3.0%
T 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 2868
33.0%
G 2615
30.1%
E 877
 
10.1%
B 819
 
9.4%
C 768
 
8.8%
D 483
 
5.6%
A 258
 
3.0%
T 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 2868
33.0%
G 2615
30.1%
E 877
 
10.1%
B 819
 
9.4%
C 768
 
8.8%
D 483
 
5.6%
A 258
 
3.0%
T 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 2868
33.0%
G 2615
30.1%
E 877
 
10.1%
B 819
 
9.4%
C 768
 
8.8%
D 483
 
5.6%
A 258
 
3.0%
T 5
 
0.1%

ID_num
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5177729
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-04-24T15:38:26.569493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0542413
Coefficient of variation (CV)0.69459753
Kurtosis8.7092628
Mean1.5177729
Median Absolute Deviation (MAD)0
Skewness2.7466168
Sum13194
Variance1.1114248
MonotonicityNot monotonic
2024-04-24T15:38:26.900650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 6217
71.5%
2 1412
 
16.2%
3 571
 
6.6%
4 231
 
2.7%
5 128
 
1.5%
6 75
 
0.9%
7 46
 
0.5%
8 13
 
0.1%
ValueCountFrequency (%)
1 6217
71.5%
2 1412
 
16.2%
3 571
 
6.6%
4 231
 
2.7%
5 128
 
1.5%
6 75
 
0.9%
7 46
 
0.5%
8 13
 
0.1%
ValueCountFrequency (%)
8 13
 
0.1%
7 46
 
0.5%
6 75
 
0.9%
5 128
 
1.5%
4 231
 
2.7%
3 571
 
6.6%
2 1412
 
16.2%
1 6217
71.5%

Group_size
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0355458
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-04-24T15:38:27.116554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5963465
Coefficient of variation (CV)0.78423511
Kurtosis3.1670958
Mean2.0355458
Median Absolute Deviation (MAD)0
Skewness1.8890173
Sum17695
Variance2.5483222
MonotonicityNot monotonic
2024-04-24T15:38:27.307087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 4805
55.3%
2 1682
 
19.3%
3 1020
 
11.7%
4 412
 
4.7%
5 265
 
3.0%
7 231
 
2.7%
6 174
 
2.0%
8 104
 
1.2%
ValueCountFrequency (%)
1 4805
55.3%
2 1682
 
19.3%
3 1020
 
11.7%
4 412
 
4.7%
5 265
 
3.0%
6 174
 
2.0%
7 231
 
2.7%
8 104
 
1.2%
ValueCountFrequency (%)
8 104
 
1.2%
7 231
 
2.7%
6 174
 
2.0%
5 265
 
3.0%
4 412
 
4.7%
3 1020
 
11.7%
2 1682
 
19.3%
1 4805
55.3%

HomePlanet
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
Earth
4709 
Europa
2175 
Mars
1809 

Length

Max length6
Median length5
Mean length5.0421028
Min length4

Characters and Unicode

Total characters43831
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEuropa
2nd rowEarth
3rd rowEuropa
4th rowEuropa
5th rowEarth

Common Values

ValueCountFrequency (%)
Earth 4709
54.2%
Europa 2175
25.0%
Mars 1809
 
20.8%

Length

2024-04-24T15:38:27.560160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T15:38:27.813494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
earth 4709
54.2%
europa 2175
25.0%
mars 1809
 
20.8%

Most occurring characters

ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6884
15.7%
t 4709
10.7%
h 4709
10.7%
u 2175
 
5.0%
o 2175
 
5.0%
p 2175
 
5.0%
M 1809
 
4.1%
s 1809
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43831
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6884
15.7%
t 4709
10.7%
h 4709
10.7%
u 2175
 
5.0%
o 2175
 
5.0%
p 2175
 
5.0%
M 1809
 
4.1%
s 1809
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43831
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6884
15.7%
t 4709
10.7%
h 4709
10.7%
u 2175
 
5.0%
o 2175
 
5.0%
p 2175
 
5.0%
M 1809
 
4.1%
s 1809
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43831
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6884
15.7%
t 4709
10.7%
h 4709
10.7%
u 2175
 
5.0%
o 2175
 
5.0%
p 2175
 
5.0%
M 1809
 
4.1%
s 1809
 
4.1%

Cabin_side
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
S
4387 
P
4306 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 4387
50.5%
P 4306
49.5%

Length

2024-04-24T15:38:28.042500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T15:38:28.267345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
s 4387
50.5%
p 4306
49.5%

Most occurring characters

ValueCountFrequency (%)
S 4387
50.5%
P 4306
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4387
50.5%
P 4306
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4387
50.5%
P 4306
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4387
50.5%
P 4306
49.5%

Transported
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
1
4378 
0
4315 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Length

2024-04-24T15:38:28.464632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T15:38:28.670013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Most occurring characters

ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4378
50.4%
0 4315
49.6%

Consumption
Real number (ℝ)

ZEROS 

Distinct2574
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean839.48951
Minimum0
Maximum28600
Zeros3805
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-04-24T15:38:28.920203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median86
Q3865
95-th percentile3837.8
Maximum28600
Range28600
Interquartile range (IQR)865

Descriptive statistics

Standard deviation1839.929
Coefficient of variation (CV)2.1917236
Kurtosis36.223627
Mean839.48951
Median Absolute Deviation (MAD)86
Skewness4.9943093
Sum7297682.3
Variance3385338.7
MonotonicityNot monotonic
2024-04-24T15:38:29.194566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3805
43.8%
1 26
 
0.3%
2 23
 
0.3%
4 22
 
0.3%
3 20
 
0.2%
5 18
 
0.2%
7 16
 
0.2%
11 15
 
0.2%
804 15
 
0.2%
6 13
 
0.1%
Other values (2564) 4720
54.3%
ValueCountFrequency (%)
0 3805
43.8%
1 26
 
0.3%
1.077766015 1
 
< 0.1%
2 23
 
0.3%
3 20
 
0.2%
4 22
 
0.3%
5 18
 
0.2%
6 13
 
0.1%
7 16
 
0.2%
7.395469138 1
 
< 0.1%
ValueCountFrequency (%)
28600 1
< 0.1%
25463.22895 1
< 0.1%
22550 1
< 0.1%
20961 1
< 0.1%
19412 1
< 0.1%
19327 1
< 0.1%
18037 1
< 0.1%
17928 1
< 0.1%
17865.12243 1
< 0.1%
17558 1
< 0.1%

Age Group
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
Young adults
5282 
Middle-aged
1607 
Minor
1550 
Senior
 
254

Length

Max length12
Median length12
Mean length10.391694
Min length5

Characters and Unicode

Total characters90335
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYoung adults
2nd rowYoung adults
3rd rowMiddle-aged
4th rowYoung adults
5th rowMinor

Common Values

ValueCountFrequency (%)
Young adults 5282
60.8%
Middle-aged 1607
 
18.5%
Minor 1550
 
17.8%
Senior 254
 
2.9%

Length

2024-04-24T15:38:29.465156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T15:38:29.680833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
young 5282
37.8%
adults 5282
37.8%
middle-aged 1607
 
11.5%
minor 1550
 
11.1%
senior 254
 
1.8%

Most occurring characters

ValueCountFrequency (%)
u 10564
11.7%
d 10103
11.2%
n 7086
 
7.8%
o 7086
 
7.8%
l 6889
 
7.6%
g 6889
 
7.6%
a 6889
 
7.6%
t 5282
 
5.8%
s 5282
 
5.8%
Y 5282
 
5.8%
Other values (7) 18983
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 10564
11.7%
d 10103
11.2%
n 7086
 
7.8%
o 7086
 
7.8%
l 6889
 
7.6%
g 6889
 
7.6%
a 6889
 
7.6%
t 5282
 
5.8%
s 5282
 
5.8%
Y 5282
 
5.8%
Other values (7) 18983
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 10564
11.7%
d 10103
11.2%
n 7086
 
7.8%
o 7086
 
7.8%
l 6889
 
7.6%
g 6889
 
7.6%
a 6889
 
7.6%
t 5282
 
5.8%
s 5282
 
5.8%
Y 5282
 
5.8%
Other values (7) 18983
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 10564
11.7%
d 10103
11.2%
n 7086
 
7.8%
o 7086
 
7.8%
l 6889
 
7.6%
g 6889
 
7.6%
a 6889
 
7.6%
t 5282
 
5.8%
s 5282
 
5.8%
Y 5282
 
5.8%
Other values (7) 18983
21.0%

Interactions

2024-04-24T15:38:17.462420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:46.752940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:49.148846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:50.732284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:09.102306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:10.624730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:12.090208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:13.748857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:15.656841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:17.689428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:47.123788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:49.314227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:50.920684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:09.273210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:10.816581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:12.278303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:13.915651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:15.878275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:17.863450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:47.333242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:49.509890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:07.928747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:09.485009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:11.001826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:12.438763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:14.118286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:16.050022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:18.032156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:47.575319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:49.672661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:08.105692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:09.676993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:11.155788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:12.585370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:14.288056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:16.215572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:18.198326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:47.808239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:49.835538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:08.253421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:09.813775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:11.283475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:12.749113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:14.464232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:16.376191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:18.337215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:48.158479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:49.999997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:08.413769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:09.955380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:11.428026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:12.898628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:14.649815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:16.554307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:18.526753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:48.492277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:50.180414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:08.583815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:10.103560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:11.607804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:13.091074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:14.941792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:16.711061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:18.745591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:48.747348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:50.333361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:08.790650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:10.259506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:11.792187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:13.362105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:15.213259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:17.100528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:18.907169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:48.994539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:37:50.550182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:08.955503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:10.424272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:11.941002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:13.559276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:15.464986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T15:38:17.295329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-24T15:38:19.161550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-24T15:38:19.761258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckID_numGroup_sizeHomePlanetCabin_sideTransportedConsumptionAge Group
0FalseTRAPPIST-1e39.0False0.00.00.00.00.0B11EuropaP00.0Young adults
1FalseTRAPPIST-1e24.0False109.09.025.0549.044.0F11EarthS1702.0Young adults
2FalseTRAPPIST-1e58.0True43.03576.00.06715.049.0A12EuropaS06807.0Middle-aged
3FalseTRAPPIST-1e33.0False0.01283.0371.03329.0193.0A22EuropaS03522.0Young adults
4FalseTRAPPIST-1e16.0False303.070.0151.0565.02.0F11EarthS1870.0Minor
5FalsePSO J318.5-2244.0False0.0483.00.0291.00.0F11EarthP1291.0Middle-aged
6FalseTRAPPIST-1e26.0False42.01539.03.00.00.0F12EarthS142.0Young adults
7TrueTRAPPIST-1e28.0False0.00.00.00.00.0G22EarthS10.0Young adults
8FalseTRAPPIST-1e35.0False0.0785.017.0216.00.0F11EarthS1216.0Young adults
9True55 Cancri e14.0False0.00.00.00.00.0B13EuropaP10.0Minor
CryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckID_numGroup_sizeHomePlanetCabin_sideTransportedConsumptionAge Group
8683FalseTRAPPIST-1e21.0False86.03.0149.0208.0329.0F22EarthP0623.0Young adults
8684TrueTRAPPIST-1e23.0False0.00.00.00.00.0G11EarthP10.0Young adults
8685FalseTRAPPIST-1e0.0False0.00.00.00.00.0A13EuropaP10.0Minor
8686FalseTRAPPIST-1e32.0False1.01146.00.050.034.0A23EuropaP085.0Young adults
8687FalseTRAPPIST-1e30.0False0.03208.00.02.0330.0A33EuropaP1332.0Young adults
8688False55 Cancri e41.0True0.06819.00.01643.074.0A11EuropaP01717.0Middle-aged
8689TruePSO J318.5-2218.0False0.00.00.00.00.0G11EarthS00.0Young adults
8690FalseTRAPPIST-1e26.0False0.00.01872.01.00.0G11EarthS11.0Young adults
8691False55 Cancri e32.0False0.01049.00.0353.03235.0E12EuropaS03588.0Young adults
8692FalseTRAPPIST-1e44.0False126.04688.00.00.012.0E22EuropaS1138.0Middle-aged

Duplicate rows

Most frequently occurring

CryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckID_numGroup_sizeHomePlanetCabin_sideTransportedConsumptionAge Group# duplicates
286TrueTRAPPIST-1e18.0False0.00.00.00.00.0G11EarthS10.0Young adults12
155TruePSO J318.5-2216.0False0.00.00.00.00.0G11EarthS10.0Minor10
259TrueTRAPPIST-1e15.0False0.00.00.00.00.0G11EarthP10.0Minor10
320TrueTRAPPIST-1e22.0False0.00.00.00.00.0G11EarthS10.0Young adults10
281TrueTRAPPIST-1e18.0False0.00.00.00.00.0F11MarsS10.0Young adults9
317TrueTRAPPIST-1e22.0False0.00.00.00.00.0G11EarthP00.0Young adults9
330TrueTRAPPIST-1e23.0False0.00.00.00.00.0G11EarthS10.0Young adults9
423TrueTRAPPIST-1e35.0False0.00.00.00.00.0F11MarsP10.0Young adults9
174TruePSO J318.5-2222.0False0.00.00.00.00.0G11EarthP10.0Young adults8
182TruePSO J318.5-2224.0False0.00.00.00.00.0G11EarthS10.0Young adults8